CN116992513A - Simulation software body-attached particle generation method adapting to complex geometry - Google Patents
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Abstract
The invention provides a method for generating simulation software body-attached particles adapting to complex geometry, and belongs to the technical field of numerical simulation industrial software. The method greatly shortens the time of particle generation through reasonable mapping and dimension reduction problem domains; the particle generation of the three-dimensional surface is simplified to the particle generation of the two-dimensional plane by the two-dimensional plane view angle. The generation of particles is reduced to one-dimensional linearity through a hash strategy, and the generation time of the body-attached particles on the three-dimensional surface is reduced. Meanwhile, the two-dimensional plane view angle ensures the body adhesion property of the generated particles, and the uniformity of the generated particles is ensured by sequencing and de-weighting the one-dimensional particles. The method obviously accelerates the generation speed of particles, ensures the body adhesion of the particles, can retain fine structural details of antennas and the like in geometric configuration, and solves the problems of long generation time, non-body adhesion, non-uniformity and the like of the three-dimensional surface particles of the simulation software.
Description
Technical Field
The invention relates to a method for generating simulated software body-attached particles adapting to complex geometry, and belongs to the technical field of numerical simulation industrial software.
Background
The simulation software is taken as an important component of industrial software and is a powerful tool for guiding product design, research and development and test. For example, the simulation software can simulate various scenes such as tsunami, automobile wading and the like, and help to cope with natural disasters, design automobile shapes and the like. The specific implementation means of the simulation software mainly comprises a grid type method and a particle type method. The particle method is an important supplement to the traditional grid method, and is particularly suitable for analog simulation aiming at complex problems such as large deformation, dynamic boundaries and the like.
The spatial distribution of particles has a great influence on the calculation accuracy, stability, and the like of the particle-based method. The ideal particle distribution is uniform and accurately describes geometric and physical information. Currently, the existing particle generation methods mainly include the following methods:
1. lattice method. The particles are generated directly on the lattice vertices in the three-dimensional tiling. The particles produced by the lattice method are theoretically most uniform. However, it requires a large amount of calculation to determine the topological relation between each particle and the geometric configuration, including calculation of the shortest distance from the particle to the configuration, and determination of the internal and external relation of the particle in the configuration. Moreover, the generated particles deviate greatly from the initial geometric configuration, a unified and efficient body attaching method is not available, and a certain degree of accuracy is lost.
2. And (5) a grid method. The particle space distribution can be obtained rapidly on the basis of the existing grid. The particles can directly take nodes, side midpoints, surface centroid, body centers and the like of the grid. Particles are generated according to a mature grid type method, and the method is visual and convenient. However, generating a uniform grid that accurately maintains geometric information often requires iterative analysis of the topological information in the grid, which is time consuming and difficult to obtain a high quality grid that accommodates complex geometries.
3. Physical bonding. The particle distribution is optimized based on the initial geometry, in combination with physical parameters and physical processes. For example, CAD-BPG proposed by team Hu Xiangyu in 2021, first generates particles based on lattice method, then substitutes the physical equation set in combination with level-set method to optimize the position of the particles. In 2023, SPHydro developed by team Sun Pengnan also borrowed this approach. The physical particle generation method is combined, multiple implementations can be realized, certain physical problems can be more adapted, and the generated particles are relatively uniform and are attached to the body. However, the computation of its physical processes requires excessive computing resources.
In summary, the conventional simulation software particle generation method has various problems such as slow speed and no sticking.
Disclosure of Invention
Aiming at the defects and shortcomings of the prior art, the invention provides a simulation software body-attached particle generation method suitable for complex geometry, which aims to solve the technical problems of long time, no body attachment, non-uniformity and the like of the generation of the simulation software three-dimensional surface particles.
The time for generating particles can be greatly shortened through reasonable mapping and dimension reduction problem fields. Particle generation of a three-dimensional surface can be simplified to particle generation of a two-dimensional plane by a two-dimensional plane view angle. Further, particle generation can be reduced to one-dimensional linearity through a hashing strategy. Thus, the generation time of the body-attached particles on the three-dimensional surface is greatly reduced. Meanwhile, the two-dimensional plane view angle ensures the body adhesion property of the generated particles, and the uniformity of the generated particles is ensured by sequencing and de-weighting the one-dimensional particles.
In order to achieve the above purpose, the present invention adopts the following technical scheme.
First, the conceptual contents related to the present invention will be described.
1. And (5) sticking the body. Is a point-to-face relationship, representing a point on a face. The adherent particles refer to particles on the surface.
2. Lattice. Is a periodic geometry in three-dimensional space. Cubes and the like are common.
3. Stacking. Is closely packed or inlaid in space, and is formed by closely packing polyhedrons or closely packing high-dimensional lattices.
4. Geometric configuration. The geometric body describing the internal structure, external surface of the object, is composed of points, lines, faces, bodies, etc.
5. Topology. Including the relationship of points, lines, planes, connections between volumes, distances, etc. in geometric space.
6. And (5) a grid. Is a basic calculation unit of the method such as the basic unit of industrial solid modeling, finite element and the like. It is composed of nodes, edges, faces, etc. and can be added with other physical parameter information.
Stl file (STereoLithography). Is a file format representing the geometry of a three-dimensional surface, consisting of a series of triangles with vertex coordinate information.
Vtk file (Visualization Toolkit, visualization tool box). Is a data set file that can carry a variety of information. Particle data with various physical parameter values may be stored.
A method for generating simulated software body-attached particles adapting to complex geometry comprises STL triangular plate particle generation, lattice hash and sequencing de-duplication. The method specifically comprises the following steps:
step 1: setting parameter values including initial inter-particle distancemarginSTL file path, normal vector inside-outside direction, etc.
Step 2: and analyzing the STL file describing the geometric configuration to obtain vertex coordinates and normal vector information of each triangular surface.
Step 3: generating satisfied band coefficientsw tri Is the primary inter-particle distance of (1)marginThe initial particles of each triangular surface of the matrix are stored in an initial particle arrayarrayTrisIs a kind of medium. As shown in fig. 2.
Preferably, the coefficientsw tri The value range of (5) is (0.2,1)]。
Step 4: computing hash initial particle arraysarrayTrisP-coordinate of each particlep x ，p y ，p z ) And calculating the minimum value of each axis of the domainp xmin ，p ymin ，p zmin ) Dividing the difference by the band coefficientw lattice Is set, and the hash value is stored in the lattice hash arrayarrayHashIs a kind of medium. Wherein, preferably, the coefficientw lattice The value range of (5) is (0.3,1)]。p x The x-axis coordinate value representing the particle p,p y the y-axis coordinate value representing the particle p,p z a y-axis coordinate value representing the particle p;p xmax 、p xmin 、p ymax 、p ymin 、 p zmax 、p zmin respectively all points in the geometric configurationx、 y、 zMaximum and minimum values on the axis.
The hash function ensures that particles with the same hash value within the computational domain must be in the same lattice.
Step 5: array according to lattice hasharrayHashFor initial particle arraysarrayTrisAnd sequencing.
Step 6: sequential scanning of ordered lattice hash arraysarrayHashWill bearrayHashCorresponding particle array of medium continuous equivalentarrayTrisCombining the particles in the matrix and storing the particles in the matrix of target particlesarrayPartis. Such a particle deduplication process requires only a linear time. And is also well suited for further acceleration in parallel. When the particles are combined, the coordinates of the continuous particles with the hash value are summed and averaged, and the unit normal vectors of the particles are summed.
The hash equivalent particles refer to particles in the same crystal lattice. The initial particles generated by the triangular plates are more dense at common points and edges, and the initial particles on the points and edges are more weighted, so that the shape characteristics of geometric configuration can be reserved.
Step 7: offset [ ]p xmin ，p ymin ，p zmin ) Repeating the steps 4-6 for several times to obtain the target particle arrayarrayPartis。
Preferably, if the lattice spacing coefficientw lattice <=0.6, the number of offsets is 7-8; if the lattice spacing coefficient is 0.6<w lattice <The number of offsets is 1-3 times, =1.
Step 8: outputting the target particlesarrayPartisParticle information in the array.
By the technical means, the generation of the simulated software body-attached particles is realized.
Advantageous effects
Compared with the prior art, the method has the following advantages:
1. the method significantly accelerates the particle generation rate. Only the triangular surface is traversed to generate the preliminary particle distribution, and the whole three-dimensional problem space is not required to be searched. The three-dimensional problem is further reduced to a one-dimensional problem through a reasonably designed hash function. The complexity of the problem is greatly reduced. After the lattice hash array is ordered, a great amount of time is not required to search for the neighbor particle sets in a double-loop mode.
2. The method only traverses the triangular surface to generate primary particle distribution, so that the body adhesion of particles is ensured to a great extent. Fine structural details of the antenna etc. in the geometry can be preserved. The sharp vertex will be somewhat smoothed.
3. The method generates particles meeting the requirement of inter-particle distancew tri *marginSuch that the initial particles are sufficient for subsequent hash deduplication. Multiple offset spacingw lattice *marginThe hash ensures a certain degree of uniformity.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of generating triangular surface primary particles in step 3 of the present invention.
FIG. 3 is a schematic diagram showing the comparison of the particle distribution produced by the method of the present invention with the particle distribution produced by other methods.
Detailed Description
The invention is described in further detail below with reference to the drawings and the detailed description. The described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, a method for generating a patch particle adapted to complex geometry.
This example describes the method of generating the particles of fig. 2, comprising the following steps:
step 1: an STL file describing the geometry is prepared. Setting parameter values including initial inter-particle distancemarginSTL file path, normal vector inside and outside direction.
Step 2: and analyzing the STL file describing the geometric configuration to obtain vertex coordinates and normal vector information of each triangular surface.
Step 3: generating a particle-to-particle spacing satisfying the preliminary requirementmargin/2The initial particles of each triangular surface of the matrix are stored in an initial particle arrayarrayTrisIs a kind of medium. As shown in fig. 2. Preferably, the inter-particle distance coefficientw tri =0.5So that the initially generated particles are sufficient for subsequent hash deduplication.
Specifically, step 3 includes the steps of:
step 3.1: along one edge AB at an initial inter-particle distancemargin/2Generating a row of initial particles;
step 3.2: gradually adding a row of distances according to the corresponding high line direction of one side AB of the triangular surfacemargin/2Particles remaining in the triangular plane;
step 3.3: until the area in the high line is traversed, storing the initially generated particles into an initial particle arrayarrayTrisIs a kind of medium.
Step 4: computing hash initial particle arraysarrayTrisP-coordinate of each particlep x ，p y ，p z ) And calculating the minimum value of each axis of the domainp xmin ，p ymin ，p zmin ) And store the hash value in the lattice hash arrayarrayHashIs a kind of medium. Preferably, the lattice spacing coefficientw lattice = 0.57. Ensuring the distance between the initial particlesmarginThe particles of (a) are not drawn into the same lattice.
Specifically, step 4 includes the steps of:
step 4.1: dividing a computational domain intow lattice Multiple pitchmarginIs selected from the cube lattices herein. The calculation domain is divided into lattices, the hash function is reasonably designed, no extra space or calculation is needed, and only the size of the divided lattices is needed to be selectedw lattice *margin. To facilitate understanding, steps are taken separately.
Step 4.2: computing hash initial particle arraysarrayTrisEach particle of (a)p Coordinates, obtain lattice hash arrayarrayHash。
Hash functionhash = p xid + p yid *( l xmax +1) + p zid * (l xmax +1)* (l ymax +1). The hash function ensures that particles with the same hash value within the computational domain must be in the same lattice.
Wherein, the method comprises the following steps ofp x ，p y ，p z ) Is a particlepRespectively divided by the lattice spacing coefficientw lattice Multiple pitchmarginThen rounding downwards to obtain lattice coordinatesp xid ，p yid ，p zid ) I.e.p xid =floor((p x -p xmin )/(w lattice * margin))，p yid =floor((p y -p ymin )/(w lattice *margin))，p zid =floor((p z -p zmin )/( w lattice * margin))。floorIs a lower rounding function.w lattice For controlling the coefficient of particle density, the value interval is preferably (0.3,1)]。
Calculating the three-dimensional space size of the domainp xmax -p xmin ，p ymax -p ymin ，p zmax -p zmin ) Divided by respectivelyw lattice Multiple pitchmarginThen, the upper limit of the lattice coordinates of each axis is obtained by downward roundingl xmax ，l ymax ，l zmax ) I.e.l xmax =floor((p xmax - p xmin )/( w lattice *margin))，l ymax =floor((p ymax -p ymin )/( w lattice *margin))，l zmax =floor ((p zmax -p zmin )/( w lattice *margin))。
Wherein, p xmax 、p xmin 、p ymax 、p ymin 、p zmax 、p zmin respectively all points in the geometric configurationx, y, zMaximum and minimum values on the axis.l xmax、 l ymax、 l zmax The number of lattice columns in the x-axis, y-axis, and z-axis directions in the calculation domain is the maximum, and the coefficients of the hash function are obtained based on the lattice columns.
Step 5: array according to lattice hasharrayHashFor initial particle arraysarrayTrisAnd sequencing.
After the lattice hash array is ordered, a great amount of time is not required to search for the neighbor particle sets in a double-loop mode.
Step 6: sequential scanning lattice hash arrayarrayHashWill bearrayHashCorresponding particle array of medium continuous equivalentarrayTrisCombining the particles in the matrix and storing the particles in the matrix of target particlesarrayPartisIs a kind of medium.
When the particles are combined, the coordinates of the continuous particles with the hash value are summed and averaged, and the unit normal vectors of the particles are summed. Wherein the hash equivalent particles are also referred to as particles in the same lattice. In this strategy, the initial particles on the points and sides will be weighted more heavily, better preserving the shape characteristics of the geometry.
Step 7: preferably, the corresponding lattice spacing coefficient0.57，Offset [ ]p xmin ，p ymin ，p zmin ) 7 times and repeating the steps 4-6 to obtain uniform particle distribution. Lattice spacing coefficient0.57Ensuring that the inter-particle distance of the same lattice does not exceed the initial inter-particle distancemargin. Because the distance of the farthest particles in the cube lattice is a diagonal, the corresponding diagonal length isLess thanmargin。
Specifically, the 7-time offset vectors are respectively(shift, 0, 0), (0, shift, 0), (0, 0, shift), (shift, shift, 0), (shift, 0, shift), (0, shift, shift), (shift, shift, shift)WhereinshiftAs an amount of the offset to be used,shift = -0.57margin/2. Respectively correspond to the direction deviation along the positive x axis, the positive y axis, the positive z axis, the positive xy axis bisector, the positive xz axis bisector and the positive yz axis bisectorw lattice* marginThe distance corresponds to the distance between the normal vector vertical x-axis surface, the normal vector vertical y-axis surface, the normal vector vertical z-axis surface, the parallel z-axis edge, the parallel y-axis edge, the parallel x-axis edge, and the adjacent particles in the adjacent areas of 8 vertexes, respectively.
Step 8: outputting the target particlesarrayPartisParticle information in the array is written into the VTK file.
Figure 3 illustrates the effect of the method of the present invention on particles produced under a number of complex geometries. The upper left of figure 3 is the cardiovascular particles generated by the lattice method and the upper right is the cardiovascular particles generated by the method; the lower left is the particles of the offshore oil platform generated by the lattice method, and the lower right is the particles of the offshore oil platform generated by the method. The initial particle spacing and other parameters of the left and right methods are consistent, and the obtained particle numbers are close. As shown in the figure, the particle patch generated by the method has better effect, the uniformity of particles is guaranteed, and the speed in actual operation is increased by several orders of magnitude.
Claims (9)
1. The method for generating the simulated software patch particles adapting to the complex geometry is characterized by comprising the steps of STL triangular plate particle generation, lattice hash and sequencing de-duplication;
step 1: setting parameter values including initial inter-particle distancemarginSTL file path, normal vector inside and outside direction; step 2: analytical tracingThe STL file of the geometric configuration obtains vertex coordinates and normal vector information of each triangular surface; step 3: generating satisfying the inter-particle distance coefficientw tri Is the primary inter-particle distance of (1)marginThe initial particles of each triangular surface of the matrix are stored in an initial particle arrayarrayTrisIn (a) and (b); step 4: computing hash initial particle arraysarrayTrisP-coordinate of each particlep x ，p y ，p z ) And calculating the minimum value of each axis of the domainp xmin ，p ymin ，p zmin ) Is divided by the band lattice spacing coefficientw lattice Is set, and the hash value is stored in the lattice hash arrayarrayHashIn the process, p x the x-axis coordinate value representing the particle p,p y the y-axis coordinate value representing the particle p,p z a y-axis coordinate value representing the particle p;p xmax 、p xmin 、p ymax 、p ymin 、p zmax 、p zmin respectively all points in the geometric configurationx、 y、 zMaximum and minimum values on the axis; step 5: array according to lattice hasharrayHashFor initial particle arraysarrayTrisSequencing; step 6: sequential scanning of ordered lattice hash arraysarrayHashWill bearrayHashCorresponding particle array of medium continuous equivalentarrayTrisCombining the particles in the matrix and storing the particles in the matrix of target particlesarrayPartis；
When the particles are combined, the coordinates of the continuous particles with the hash value are summed and averaged, and the unit normal vectors of the particles are summed; wherein, the hash equivalent particles refer to particles in the same crystal lattice;
step 7: offset [ ]p xmin ，p ymin ，p zmin ) Repeating the steps 4-6 for several times to obtain the target particle arrayarrayPartisThe method comprises the steps of carrying out a first treatment on the surface of the Step 8: outputting the target particlesarrayPartisParticle information in the array.
2. The method for generating simulated software patch particles adapting to complex geometry according to claim 1, wherein in step 3, inter-particle distance coefficients are obtainedw tri The value range of (5) is (0.2,1)]。
3. The method for generating simulated software patch particles adapting to complex geometry as claimed in claim 2, wherein in step 3, inter-particle distance coefficient is calculatedw tri =0.5。
4. The method for generating the simulated software patch particles adapting to the complex geometry according to claim 1, wherein the step 3 comprises the following steps: step 3.1: along one side AB of the triangular surface at an initial inter-particle distancemargin/2Generating a row of initial particles; step 3.2: gradually adding a row of distances according to the corresponding high line direction of the triangular surface edge ABmargin/2Particles remaining in the triangular plane;
step 3.3: traversing the area in the high line, and storing the initially generated particles into an initial particle arrayarrayTrisIs a kind of medium.
5. The method for generating simulated software patch particles adapting to complex geometry as claimed in claim 1, wherein in step 4, the lattice spacing coefficient isw lattice The value range of (5) is (0.3,1)]。
6. The method for generating simulated software body particles adapting to complex geometry according to claim 5, wherein in step 4, the lattice spacing coefficient isw lattice = 0.57。
7. The method for generating the simulated software patch particles adapting to the complex geometry according to claim 1, wherein in the step 4, the method comprises the following steps: step 4.1: dividing a computational domain intow lattice Multiple pitchmarginIs a lattice of (c);
step 4.2: computing hash initial particle arraysarrayTrisP-coordinates of each particle in the matrix to obtain a lattice hash arrayarrayHashThe method comprises the steps of carrying out a first treatment on the surface of the Hash functionhash = p xid + p yid *( l xmax +1)+ p zid * (l xmax +1)* (l ymax +1)Divided by lattice spacing coefficient, respectivelyw lattice Multiple pitchmarginThen rounding downwards to obtain lattice coordinatesp xid ，p yid ，p zid ）：
p
xid
= floor((p
x
-p
xmin
)/(w
lattice
*margin))
p
yid
= floor((p
y
-p
ymin
)/(w
lattice
*margin))
p
zid
= floor((p
z
-p
zmin
)/( w
lattice
*margin))
Wherein, flooris a lower rounding function.
8. A simulation software adapted to complex geometries as recited in claim 1A method for producing a body-adhering particle, characterized in that in step 7, if the lattice spacing coefficient isw lattice <=0.6, the number of offsets is 7-8; if the lattice spacing coefficient is 0.6<w lattice <The number of offsets is 1-3 times, =1.
9. The method for generating simulated software patch particles adapting to complex geometry according to claim 1, wherein 7 times of offset vectors are respectively(shift, 0, 0), (0, shift, 0), (0, 0, shift), (shift, shift, 0), (shift, 0, shift), (0, shift, shift), (shift, shift, shift)WhereinshiftAs an amount of the offset to be used,shift = -0.57margin/2respectively correspond to the direction deviation along the positive x axis, the positive y axis, the positive z axis, the positive xy axis bisector, the positive xz axis bisector, the positive yz axis bisector and the positive xyz axis bisectorw lattice* marginThe distance corresponds to the distance between the normal vector vertical x-axis surface, the normal vector vertical y-axis surface, the normal vector vertical z-axis surface, the parallel z-axis edge, the parallel y-axis edge, the parallel x-axis edge, and the adjacent particles in the adjacent areas of 8 vertexes, respectively.
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